A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

I. the origin of species by means of natural selection

Initial-value ordinary differential equation solution via variable order Adams method (SIVA/DIVA) package is collection of subroutines for solution of nonstiff ordinary differential equations. There are versions for single-precision and double-precision arithmetic. Requires fewer evaluations of derivatives than other variable-order Adams predictor/ corrector methods. Option for direct integration of second-order equations makes integration of trajectory problems significantly more efficient. Written in FORTRAN 77.

Solving Ordinary Differential Equations

In this paper, the relationship between bilateral filtering and anisotropic diffusion is examined. The bilateral filtering approach represents a large class of nonlinear digital image filters. We first explore the connection between anisotropic diffusion and adaptive smoothing, and then the connection between adaptive smoothing and bilateral filtering. Previously, adaptive smoothing was considered to be an inconsistent approximation to the nonlinear diffusion equation. We extend adaptive smoothing to make it consistent, thus enabling a unified viewpoint that relates nonlinear digital image filters and the nonlinear diffusion equation.

Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation

A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift

An original signal extraction procedure is applied to database of 146 base nucleosome core DNA sequences from C. elegans (S. M. Johnson et al. Genome Research 16, 1505–1516, 2006). The positional preferences of various dinucleotides within the 10.4 base nucleosome DNA repeat are calculated, resulting in derivation of the nucleosome DNA bendability matrix of 16x10 elements. A simplified one-line presentation of the matrix (“consensus” repeat) is…A(TTTCCGGAAA)T…. All 6 chromosomes of C. elegans conform to the bendability pattern. The strongest affinity to their respective positions is displayed by dinucleotides AT and CG, separated within the repeat by 5 bases. The derived pattern makes a basis for sequence-directed mapping of nucleosome positions in the genome of C. elegans. As the first complete matrix of bendability available the pattern may serve for iterative calculations of the species-specific matrices of bendability applicable to other genomic sequences.

Nucleosome DNA bendability matrix (C. elegans).

A system and method of image processing for smoothing, denoising, despeckling and sharpening scanned document images which is performed prior to a compression. The scanned image is selectively smoothed by anisotropic diffusion filtering in a single iteration with a 3x3 kernel, which provides denoising, edge-preserving smoothing. The smoothed image data is then selectively sharpened using variable contrast mapping that provides overshoot-free variable-sharpening and despeckling. Image quality is improved while increasing compressibility of the image.

Method for enhancing compressibility and visual quality of scanned document images

Bilateral filtering has recently been proposed as a noniterative alternative to anisotropic diffusion. In both these approaches, images are smoothed while edges are preserved. Unlike anisotropic diffusion, bilateral filtering does not involve the solution of partial differential equations and can be implemented in a single iteration. Despite the difference in implementation, both methods are designed to prevent averaging across edges while smoothing an image. Their similarity suggests they can somehow be linked. Using a generalized representation for the intensity, we show that both can be related to adaptive smoothing. As a consequence, bilateral filtering can be applied to denoise and coherence-enhance degraded images with approaches similar to anisotropic diffusion.

http://www.hpl.hp.com/techreports/2000/HPL-2000-18R1.pdf

Danny Barash

Papers

Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation

A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift

Nucleosome DNA bendability matrix (C. elegans).

Method for enhancing compressibility and visual quality of scanned document images

Bilateral Filtering and Anisotropic Diffusion: Towards a Unified Viewpoint